This is to provide an update of the current status of the phase 1 of the smart grid project (note: description of this project can be found in the thread "Smart Grid Project : ML Predictions").
In the phase 1 we use WSO2 ML to predict the next market clearing price for the next 15 min period. The clearing price prediction is done by first training the machine learning algorithm using the weather data. The training data set contains clearing prices, wind speed and solar radiation that was observed during the previous market clearing. Using this model and the weather data of the next 15 min, we predict the clearing price for the 15 min period. This information is then sent to the GridLab-D (power distribution system simulator). GridLab-D is configured to simulate 629 houses, large and small wind turbines and a set of solar panels connected together and to power grid via IEEE-12 distribution feeder. The consumers (houses etc) and generators (i.e. wind turbines etc) within the GridLab-D use the predicted clearing price to decide how they bid. The overall objective is to minimize the costs associated with drawing additional energy (i.e. from main grid), due to temporary variations in the energy generated by renewable resources. 1) To predict the next clearing price we have tried different ML algorithms @Sanjaya could you please provide the summary of results (accuracy etc) we got under different machine learning algorithms (i.e. random forest regression etc) 2) In GridLab-D we compute how much energy is saved and the cost savings @Nihla could you please provide the cost/energy savings we got as a result of using ML Thanks -- Malith Jayasinghe WSO2, Inc. (http://wso2.com) Email : [email protected] Mobile : 0770704040 Lean . Enterprise . Middleware
_______________________________________________ Architecture mailing list [email protected] https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture
